Metrics question

There is a spike in Lyft ride cancellations this week. Why could this be the case?

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What this question tests

Tests root-cause diagnosis of a metric spike using a structured hypothesis-and-segmentation approach.

How to approach it

  1. Split cancellations by who initiated them, rider versus driver, since the causes and fixes differ completely.
  2. Hypothesize rider-side causes: longer-than-usual ETAs from a driver shortage, surge pricing prompting a cancel-and-rebook, or an app matching bug.
  3. Hypothesize driver-side causes: low fares relative to trip length, a problematic app update, or driver incentive changes reducing supply in certain zones.
  4. Segment the data by time of day, city or zone, and platform to see if this is localized or a global issue.
  5. Cross-reference with any recent app releases or known supply shortages in the spike window before proposing a fix.

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